[R] convergence error (lme) which depends on the version of nlme (?)

[Leo Gürtler]

Dear list members,

the following hlm was constructed:

hlm <- groupedData(laut ~ design | grpzugeh, data = imp.not.I)

the grouped data object is located at and can be downloaded:

www.anicca-vijja.de/lg/hlm_example.Rdata

The following works:

library(nlme)
summary( fitlme <- lme(hlm) )

with output:

...
       AIC      BIC    logLik
  425.3768 465.6087 -197.6884

Random effects:
 Formula: ~design | grpzugeh
 Structure: General positive-definite
             StdDev    Corr               
(Intercept)  0.3772478 (Intr) dsgn:8 dsgn:7
designmit:8  0.6776543  0.183             
designohne:7 0.6619983 -0.964  0.086      
designohne:8 1.0680576 -0.966  0.077  1.000
Residual     1.3468816                    

Fixed effects: laut ~ design
                 Value Std.Error  DF   t-value p-value
(Intercept)   3.857143 0.2917529 102 13.220579  0.0000
designmit:8  -0.285714 0.4417919 102 -0.646717  0.5193
designohne:7 -0.107143 0.4383878 102 -0.244402  0.8074
designohne:8  0.607143 0.5408713 102  1.122527  0.2643
 Correlation:
             (Intr) dsgnm:8 dsgn:7
designmit:8  -0.451              
designohne:7 -0.775  0.363       
designohne:8 -0.763  0.304   0.699

Standardized Within-Group Residuals:
       Min         Q1        Med         Q3        Max
-2.5074669 -0.4530573  0.1755326  0.5837670  2.3700004

Number of Observations: 112
Number of Groups: 7


The following does _not_ work and leads to a convergence error:

fitlme1 <- lme(laut ~ design, random = ~ design | grpzugeh, data = hlm)
Fehler in lme.formula(laut ~ design, random = ~design | grpzugeh, data = 
hlm) :
        iteration limit reached without convergence (9)

This was tried with

R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.2.0  (2005-10-06 r35749)

Using another R version (2.1.0, also windows with nlme version built 
under R 2.1.1) , it works. Thus, what's the problem then? I tried 
without the random effects, i.e.

random = ~ 1 | grpzugeh

This works. Comparing both calls on the version R2.1.0 that goes well, 
the following differences in the output of the random effects can be 
identified:

summary( fitlme <- lme(hlm) )

<-->
Random effects:
 ...
  Structure: General positive-definite
</-->
compared to

summary(lme(laut ~ design, random = ~ design | grpzugeh, data = hlm))

<-->
Random effects:
  ...
  Structure: General positive-definite, Log-Cholesky parametrization
</-->

The estimates of the fixed effects are similar, the S.E.s not.
The random effects are different, too. AIC/BIC/logLik are slightly 
different.

Thus my question:

1) Do I have overseen a switch for the structure of the random effects? 
Is something wrong with the call/ formular?
2) What is the cause of the convergence error which seems to depend on 
the built of R/nlme?


Thank you very much. Best wishes,

leo gürtler


-- 

email: leog at anicca-vijja.de
www: http://www.anicca-vijja.de/